Occupancy tracking using wireless signal distortion

ABSTRACT

An occupancy tracking device configured to establish a network connection with an access point and to capture wireless signal distortion information for the network connection. The device is further configured to generate statistical metadata for the wireless signal distortion information. The device is further configured to input the wireless signal distortion information and the statistical metadata for the wireless signal distortion information into a machine learning model. The machine learning model is configured to determine a predicted occupancy level based on the wireless signal distortion information and the statistical metadata for the wireless signal distortion information. The predicted occupancy level indicates a number of people that are present within with the space. The device is further configured to obtain the predicted occupancy level from the machine learning model and to control a Heating, Ventilation, and Air Conditioning (HVAC) system based on the predicted occupancy level.

TECHNICAL FIELD

The present disclosure relates generally to occupancy tracking, and morespecifically to occupancy tracking using wireless signal distortion.

BACKGROUND

Determining the number of people that are present within a space (e.g. ahome or office) poses a technical challenge for existing Heating,Ventilation, and Air Conditioning (HVAC) systems. Existing HVAC systemstypically use a proximity sensor or a motion detection sensor todetermine whether a space is occupied. Proximity sensors and motiondetection sensors can only provide a binary indication of whether aspace is occupied. This means that existing HVAC systems can only detectwhether someone is present within a space, but they are unable todetermine the number of people that are present within the space. Inaddition, existing HVAC systems are also unable to identify who ispresent within the space. Without the ability to identify the peoplethat are present within a space, existing HVAC systems are unable toprovide personalized HVAC settings that corresponds with the people thatare present within the space.

SUMMARY

The system disclosed in the present application provides a technicalsolution to the technical problems discussed above by providing anoccupancy tracking system that is configured to predict the number ofpeople that are present within a space based on various types ofenvironmental information within the space. In contrast to existing HVACsystems, the occupancy tracking system is configured to indicate whethera space is occupied as well as the number of people that are presentwithin the space. By determining the number of people that are presentwithin a space, the occupancy tracking system is able to provide bettercontrol and management of the HVAC system. This process allows theoccupancy tracking system to more efficiently manage and operate theHVAC system based on the number of people that are present within aspace.

In addition, the occupancy tracking system may be configured to identifywho is present within space. This process allows the occupancy trackingsystem to control the HVAC system using personalized settings based onthe person's personal preferences. For example, the occupancy trackingsystem may identify a person that is present within the space, identifyHVAC settings (e.g. preferred set point temperatures) for the identifiedperson, and then control the HVAC system using the identified HVACsettings. This process allows the occupancy tracking system toefficiently manage the HVAC system while accommodating the preferencesof the people that are present within a space.

The disclosed system provides several practical applications andtechnical advantages which include a process for determining the numberof people that are present within a space based on voices that are heardwithin the space. Unlike existing HVAC systems that rely on proximitysensors and motion sensors, the occupancy tracking system captures andprocesses audio signals from within a space to determine the number ofpeople that are present within the space based on the presence of voicesthat are captured in the audio signals. This process also allows theoccupancy tracking system to filter out the voices that come fromelectronic devices (e.g. a television and radio) to provide a moreaccurate prediction of the number of people that are present within thespace. The occupancy tracking system may then efficiently control theoperation of the HVAC system based on the determined number of peoplethat are present within the space.

The disclosed system also includes a process for determining the numberof people that are present within a space based on the user devices thatare detected within the space. In this case, the occupancy trackingsystem identifies the devices that are connected to an access pointwithin a space to determine the number of people that are present withinthe space. This process also allows the occupancy tracking system todistinguish between electronic devices (e.g. televisions and desktopcomputers) that are always present in a space and personal user devicesto provide a more accurate prediction of the number of people that arepresent within the space. The occupancy tracking system may thenefficiently control the operation of the HVAC system based on thedetermined number of people that are present within the space.

The disclosed system also includes a process for determining the numberof people that are present within a space based on a signal strength ofa network connection between a thermostat and an access point. Theoccupancy tracking system monitors the signal strength of the networkconnection over time to determine the number of people that are presentwithin the space based on the measured signal strength. In a firstphase, the occupancy tracking system collects training data that is usedto train a machine learning model. The machine learning model isgenerally configured to predict the number of people that are presentwithin a space based on wireless signal distortion information. Thetraining data comprises signal strength measurements over a period oftime and statistical metadata for the signal strength measurements. In asecond phase, the occupancy tracking system collects current wirelesssignal distortion information (e.g. current signal strengthmeasurements) and provides the collected information to the trainedmachine learning model to obtain a predicted occupancy level for thespace. The occupancy tracking system may then efficiently control theoperation of the HVAC system based on the determined number of peoplethat are present within the space.

The disclosed system also includes a process for determining the numberof people that are present within a space based on a combination ofenvironmental information. For example, the occupancy tracking systemmay determine the number of people that are present within a space basedon the voices that are heard within the space, the user devices that aredetected within space, and/or the signal strength of a networkconnection between a thermostat and an access point. This process allowsthe occupancy tracking system to distinguish between electronic devices(e.g. televisions and desktop computers) that are always present in aspace and personal user devices to provide a more accurate prediction ofthe number of people that are present within the space. This processalso allows the occupancy tracking system to filter out voices that comefrom electronic sound sources (e.g. televisions and radios) rather thanpeople which also provides a more accurate prediction of the number ofpeople that are present within the space. The occupancy tracking systemmay then efficiently control the operation of the HVAC system based onthe determined number of people that are present within the space.

In one embodiment, the system comprises a device that is configured toestablish a network connection with an access point and to capturewireless signal distortion information (e.g. signal strengthmeasurements) for the network connection. After capturing the wirelesssignal distortion information, the device generates statistical metadatafor the wireless signal distortion information. The device then inputsthe wireless signal distortion information and the statistical metadatafor the wireless signal distortion information into a machine learningmodel. The machine learning model is configured to determine a predictedoccupancy level based on the wireless signal distortion information andthe statistical metadata for the wireless signal distortion information.The predicted occupancy level indicates the number of people that arepresent within the space. In response to providing the wireless signaldistortion information and the statistical metadata to the machinelearning model, the device obtains the predicted occupancy level fromthe machine learning model and controls a Heating, Ventilation, and AirConditioning (HVAC) system based on the predicted occupancy level.

In another embodiment, the system comprises a device configured torecord a plurality of sound samples over a predetermined time period.After recording the sound samples, the device computes an audiosignature for each sound sample. The audio signature includes anumerical value that uniquely identifies the characteristics within anaudio signal. The device also determines a direction of arrival for eachsound sample. The device then populates entries in the voice data logfor the sound samples and identifies clusters based on an audiosignature that is associated with the populated entries. The device thendetermines a predicted occupancy level based on the number of clustersthat are identified and controls an HVAC system based on the predictedoccupancy level.

In another embodiment, the system comprises a device configured toidentify devices connected to the access point over a predetermined timeperiod. The device then populates entries in a device log for theidentified devices. The device then determines a presence value for eachdevice. The presence value indicates the amount of time that a devicewas present during the predetermined time period. The device thenidentifies entries that are associated with a presence value that isless than a presence threshold value and associates the identifiedentries with a user device classification. The device will then identifyclusters for the entries that are associated with a user deviceclassification, determine a predicted occupancy level based on thenumber of clusters that are identified, and control an HVAC system basedon the predicted occupancy level.

In another embodiment, the system comprises a device that receives soundsamples, identifies voices within the sound samples, and determines afirst occupancy level based on the identified voices. The device maythen identify user devices connected to an access point and determine asecond occupancy level based on the user devices that are connected tothe access point. The device may then measure a signal strength of anetwork connection with the access point and determine a third occupancylevel based on the signal strength of the network connection with theaccess point. The device then determines a predicted occupancy levelbased on the first occupancy level, the second occupancy level, and thethird occupancy level and controls an HVAC system based on the predictedoccupancy level.

Certain embodiments of the present disclosure may include some, all, ornone of these advantages. These advantages and other features will bemore clearly understood from the following detailed description taken inconjunction with the accompanying drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following brief description, taken in connection with theaccompanying drawings and detailed description, wherein like referencenumerals represent like parts.

FIG. 1 is a schematic diagram of an embodiment of an occupancy trackingsystem for heating, ventilation, and air conditioning (HVAC) systems;

FIG. 2 is a flowchart of an embodiment of an occupancy tracking processusing sound recognition;

FIG. 3 is an example of the occupancy tracking system employing theoccupancy tracking process using sound recognition;

FIG. 4 is a flowchart of an embodiment of an occupancy tracking processusing user device detection;

FIGS. 5 and 6 are an example of the occupancy tracking system employingthe occupancy tracking process using user device detection;

FIG. 7 is a flowchart of an embodiment of an occupancy tracking processusing wireless signal distortion;

FIGS. 8 and 9 are an example of the occupancy tracking system employingthe occupancy tracking process using wireless signal distortion;

FIG. 10 is a flowchart of an embodiment of an occupancy tracking processusing environmental information;

FIG. 11 is an embodiment of an occupancy tracking device for theoccupancy tracking system; and

FIG. 12 is a schematic diagram of an embodiment of an HVAC systemconfigured to integrate with the occupancy tracking system.

DETAILED DESCRIPTION System Overview

FIG. 1 is a schematic diagram of an embodiment of an occupancy trackingsystem 100 for heating, ventilation, and air conditioning (HVAC) systems104. The occupancy tracking system 100 is generally configured tocollect various type of data (e.g. audio signals, wireless signalstrength information, device information, etc.) that is associated withthe conditions within a physical location, to determine a predictednumber of people that are present within the physical location, and tocontrol the HVAC system 104 based on the predicted number of people thatare present within the physical location.

Existing HVAC systems typically use a proximity sensor or a motiondetection sensor to determine whether a space (e.g. a home or office) isoccupied. Proximity sensors and motion detection sensors can onlyprovide a binary indication of whether a space is occupied. These typesof sensors are unable to determine the number of people that are presentwithin the space. In contrast, the occupancy tracking system 100 isconfigured to indicate whether a space is occupied as well as the numberof people that are present within the space. By determining the numberof people that are present within a space, the occupancy tracking system100 is able to provide better control and management of the HVAC system104. For example, when a large number of people are detected within aspace, the occupancy tracking system 100 may adjust the settings of theHVAC system 104 to reach its target set point temperature in a shorteramount of time. This process allows the occupancy tracking system 100 toefficiently manage the HVAC system 104 based on the number of peoplethat are present within a space.

In addition, existing HVAC systems are also unable to identify who ispresent within a space. In contrast, the occupancy tracking system 100is configured to identify who is present within space. This processallows the occupancy tracking system 100 control the HVAC system 104using personalized settings based on the person's personal preferences.For example, the occupancy tracking system 100 may identify a personthat is present within the space, identify HVAC settings (e.g. preferredset point temperatures) for the identified person, and control the HVACsystem 104 using the identified HVAC settings. This process allows theoccupancy tracking system 100 to efficiently manage the HVAC system 104while accommodating the preferences of the people that are presentwithin a space.

In one embodiment, the occupancy tracking system 100 comprises athermostat 102, an HVAC system 104, and an access point 106 that are insignal communication with each other over a network 108. The network 108may be any suitable type of wireless and/or wired network including, butnot limited to, all or a portion of the Internet, an Intranet, a privatenetwork, a public network, a peer-to-peer network, the public switchedtelephone network, a cellular network, a local area network (LAN), ametropolitan area network (MAN), a personal area network (PAN), a widearea network (WAN), and a satellite network. The network 108 may beconfigured to support any suitable type of communication protocol aswould be appreciated by one of ordinary skill in the art.

HVAC System

An HVAC system 104 is generally configured to control the temperature ofa space. Examples of a space include, but are not limited to, a room, ahome, an apartment, a mall, an office, a warehouse, or a building. TheHVAC system 104 may comprise the thermostat 102, compressors, blowers,evaporators, condensers, and/or any other suitable type of hardware forcontrolling the temperature of the space. An example of an HVAC system104 configuration and its components are described in more detail belowin FIG. 12. Although FIG. 1 illustrates a single HVAC system 104, alocation or space may comprise a plurality of HVAC systems 104 that areconfigured to work together. For example, a large building may comprisemultiple HVAC systems 104 that work cooperatively to control thetemperature within the building.

Access Points

An example of an access point 106 is a wired or wireless modem orrouter. An access point 106 is generally configured to providenetworking capabilities to the thermostat 102, the HVAC system 104, andother devices within the space. The access point 106 may be configuredto establish a network connection with the thermostat 102 and to providedata to the thermostat 102. For example, the access point 106 may beconfigured to send information about the devices that are connected tothe access point 106 to the thermostat 102. In other examples, theaccess point 106 may be configured to send any other suitable type ofdata to the thermostat 102. Examples of the access point 106 inoperation are described below in FIGS. 4-10.

Thermostat

The thermostat 102 is generally configured to collect various type ofdata (e.g. audio signals, wireless signal strength information, deviceinformation, etc.) about the conditions within a space, to determine apredicted number of people that are present within the space, and tocontrol the HVAC system 104 based on the predicted number of people thatare present within the space. Examples of the thermostat 102 inoperation are described below in FIGS. 2-12.

In one embodiment, the thermostat 102 comprises an occupancy detectionengine 110, a microphone array 112, and a memory 114. The thermostat 102may further comprise a graphical user interface, a display, a touchscreen, buttons, knobs, or any other suitable combination of components.Additional details about the hardware configuration of the thermostat102 are described in FIG. 11. The occupancy detection engine 110 isgenerally configured to predict the number of people that are presentwithin a space and to control the operation of the HVAC system 104 basedon the predicted number of people within the space. As an example, theoccupancy detection engine 110 may be configured to adjust a set pointtemperature based on the predicted number of people within the space. Asanother example, the occupancy detection engine 110 may be configured totransition the HVAC system 104 into a low power mode or out of a lowpower mode based on the predicted number of people within the space. Inother examples, the occupancy detection engine 110 may be configured toperform any other suitable type of operation to control the operation ofthe HVAC system 104 based on the predicted number of people within thespace. Examples of the occupancy detection engine 110 in operation aredescribed in FIGS. 2-10.

The memory 114 is configured to store a device log 116, a voice data log118, a machine learning model 120, and/or any other suitable type ofdata. The device log 116 is generally configured to store deviceinformation that is associated with devices that are connected to theaccess point 106 within a space. Examples of devices include, but arenot limited to, televisions, radios, desktop computers, smartappliances, Internet-of-things devices, smartphones, tablets,smartwatches, smart glasses, laptop computers, or any other suitabletype of electronic device. The thermostat 102 is configured to processand use the stored device information to predict the number of peoplethat are present within the space based on the devices that arecurrently connected to the access point 106. The device log 116comprises a plurality of entries 122 that are each associated with adevice that is connected to the access point 106. In one embodiment,each entry 122 comprises a timestamp 124 that identifies when a devicewas detected, a device identifier 126 that identifies the device, and adevice classification 128 that identifies the device type. In otherexamples, each entry 122 may comprise any other suitable type orcombination of information that is associated with the devices that areconnected to the access point 106.

The voice data log 118 is generally configured to store information thatis associated with sound samples that are captured within a space. Thethermostat 102 is configured to process and use the stored sound samplesto predict the number of people that are present within the space basedon the voices that are detected within the space. The voice data log 118comprises a plurality of entries 130 that are each associated with anaudio signal 306 that is recorded within the space. In one embodiment,each entry 130 comprises a timestamp 132 that identifies when an audiosignal 306 was recorded, an audio signature 134 that identifiescharacteristics of sounds within the audio signal 306, and a directionof arrival 136 that identifies a location of a sound source for theaudio signal 306. In other examples, each entry 130 may comprise anyother suitable type or combination of information that is associatedwith an audio signal 306.

Examples of machine learning models 120 include, but are not limited to,a multi-layer perceptron or any other suitable type of neural networkmodel. The machine learning models 120 are generally configured tooutput a predicted number of people that are within a space based on thecurrent signal strength of a network connection between the thermostat102 and the access point 106. In one embodiment, a machine learningmodel 120 is configured to receive wireless signal distortioninformation (e.g. a signal strength measurement 144) and statisticalmetadata 146 for the wireless signal distortion information as inputsand to output a predicted occupancy level based on the wireless signaldistortion information and the statistical metadata 146 for the wirelesssignal distortion information. The statistical metadata 146 comprisesstatistical metrics that are associated with a measured signal strengthover a period of time. For example, the statistical metadata 146 maycomprise a mean value, a standard deviation value, a skew value, avariance value, a maximum signal strength value, a minimum signalstrength value, or any other suitable type of statistical informationassociated with the wireless signal distortion information.

The machine learning model 120 is trained using training data 138. Thetraining data 138 comprises a plurality of entries 140. In oneembodiment, each entry 140 comprises a timestamp 142, a signal strengthmeasurement 144, statistical metadata 146 for the signal strengthmeasurement 144, and the number of occupants that are present within aspace. During the training process, the machine learning model 120determines weight and bias values for a mapping function that allows themachine learning model 120 to map different combinations of wirelesssignal distortion information (e.g. a signal strength measurement 144),statistical metadata 146, and occupancy information to a predictedoccupancy level. Through this process, the machine learning model 120 isconfigured to determine a predicted occupancy level for a space thatbest corresponds with the wireless signal distortion information (e.g. asignal strength measurement 144), statistical metadata 146, and theoccupancy information. The occupancy detection engine 110 may train themachine learning model 120 using any suitable technique as would beappreciated by one of ordinary skill in the art.

Occupancy Tracking Process Overview

FIGS. 2 and 3 are an example a process for determine the number ofpeople that are present in a space using sound recognition. FIGS. 4-6are an example a process for determine the number of people that arepresent in a space using user device detection. FIGS. 7-9 are an examplea process for determine the number of people that are present in a spaceusing wireless signal distortion. FIG. 10 is an example a process fordetermine the number of people that are present in a space using acombination of environmental information.

Occupancy Tracking Process Using Sound Recognition

FIG. 2 is a flowchart of an embodiment of an occupancy tracking process200 using sound recognition. The occupancy tracking system 100 mayemploy process 200 to determine the number of people that are presentwithin a space based on voices that are heard within the space. Unlikeexisting HVAC systems that rely on proximity sensors and motion sensors,the occupancy tracking system 100 is able to capture and process audiosignals 306 from within a space. The occupancy tracking system 100determines the number of people that are present within the space basedon the voices that are captured in the audio signals 306. Process 200also allows the occupancy tracking system 100 to filter out the voicesfrom electronic devices (e.g. televisions and radio) to provide a moreaccurate prediction of the number of people that are present within thespace. Process 200 also allows the occupancy tracking system 100 toefficiently control the operation of the HVAC system 104 based on thedetermined number of people that are present within the space.

At step 202, the thermostat 102 records a sound sample. The thermostat102 uses the microphone array 112 to capture a sound sample of an audiosignal 306 from within a space. The thermostat 102 uses the microphonearray 112 to record sounds and voices that are present within the space.The thermostat 102 may be configured to record sound samplescontinuously or at predetermined time intervals. For example, thethermostat 102 may be configured to continuously record sound samples inthree-minute segments. In this example, the thermostat 102 records a newsound sample every three minutes and each sound sample has a duration ofthree minutes. In other examples, the thermostat 102 may be configuredto record sound samples at any other suitable time interval. Thethermostat 102 may also record sound samples for any other suitableduration of time. In some examples, the thermostat 102 may be configuredto record sound samples in response to detecting a noise. For instance,any noise or sound may be used as a trigger event to trigger thethermostat 102 to record sound samples.

Referring to the example shown in FIG. 3, the thermostat 102 may belocated in a space 300 (e.g. a living room area) near a television 304.In this example, a first person 302A and a second person 302B are alsolocated in the living while the television 304 is playing, Thethermostat 102 records a sound sample that comprises a first audiosignal 306A, a second audio signal 306B, a third audio signal 306C, anda fourth audio signal 306D. The first audio signal 306A corresponds witha voice from the first person 302A speaking. The second audio signal306B corresponds with a voice from the second person 302B speaking. Thethird audio signal 306C and the fourth audio signal 306D correspondswith two voices coming from the television 304.

Returning to FIG. 2 at step 204, the thermostat 102 computes an audiosignature 134 for the sound sample. An audio signature 134 is anumerical value that uniquely identifies the characteristics of thesounds (e.g. voices) within an audio signal 306. In one example, thethermostat 102 may compute an audio signature 134 by determiningMel-frequency cepstral coefficients (MFCCs) for the audio signal 306. Inthis example, the thermostat 102 may process the sound sample toidentify MFCC features or characteristics that are present within theaudio signal 306. The thermostat 102 may then generate a numeric valuebased on the identified MFCC features. In other examples, the thermostat102 may compute the audio signature 134 using any other suitable signalprocessing technique.

Continuing with the example in FIG. 3, the thermostat 102 processes therecorded sound sample and computes an audio signature 134 for each ofthe distinct voices that are found in the sound sample. Here, thethermostat 102 computes an audio signature 134 for each of the audiosignals 306 that is present in the sound sample. In this example, thethermostat 102 computes a first audio signature 134 for the first audiosignal 306A, a second audio signature 134 for the second audio signal306B, a third audio signature 134 for the third audio signal 306C, and afourth audio signature 134 for the fourth audio signal 306D.

Returning to FIG. 2 at step 206, the thermostat 102 determines directionof arrival information for the audio signature 134. The direction ofarrival 136 indicates a direction that an audio signal 306 ispropagating from. In other words, the direction of arrival 136 indicatesthe relative location of a sound source for an audio signal 306 withrespect to the thermostat 102. In one embodiment, the thermostat 102 maydetermine a direction of arrival 136 based on the order that microphonesin the microphone array 112 hear an audio signal 306. For example, thethermostat 102 may determine the direction of arrival 136 using ageneralized cross-correlation phase transformation algorithm with themicrophone array 112. In other examples, the thermostat 102 maydetermine the direction of arrival 136 using any other suitablealgorithm or technique. The thermostat 102 is configured to determine adirection of arrival 136 for each of the audio signals 306 that ispresent in the sound sample.

Continuing with the example in FIG. 3, the thermostat 102 determines afirst direction of arrival 136A for the first audio signal 306A and asecond direction of arrival 136B for the second audio signal 306B. Inthis example, the thermostat 102 determines that the third audio signal306C and the fourth audio signal 306D are both associated with the samedirection of arrival 136C. This means that the third audio signal 306Cand the fourth audio signal 306D came from the same location (i.e. thesame sound source).

Returning to FIG. 2 at step 208, the thermostat 102 creates an entry 130in the voice data log 118 for the audio signature 134. Continuing withthe example in FIG. 3, the thermostat 102 may create a first entry 130in the voice data log 118 for the first audio signal 306A. The firstentry 130 may comprise a timestamp 132 that identifies when the firstaudio signal 306A was recorded, the first audio signature 134 for thefirst audio signal 306A, and the direction of arrival 136A for the firstaudio signal 306A. In some examples, the first entry 130 may furthercomprise any other suitable type of information that is associated withthe first audio signal 306A. The thermostat 102 repeats this process tocreate a second entry 130, a third entry 130, and a fourth entry 130 forthe second audio signal 306B, the third audio signal 306C, and thefourth audio signal 306D, respectively.

At step 210, the thermostat 102 determines whether to collect more soundsamples. In one embodiment, the thermostat 102 may be configured tocollect sound samples for a predetermined amount of time. For example,the thermostat 102 may be configured to collect sound samples over atime period of nine minutes. In other examples, the thermostat 102 maybe configured to collect sound samples over any other suitable amount oftime. When the predetermined amount of time has not elapsed, thethermostat 102 will continue to collect sound samples until thepredetermined amount of time has elapsed. The thermostat 102 returns tostep 202 in response to determining to collect additional sound samples.In this case, the thermostat 102 returns to step 202 to continuerecording additional sound samples. After the predetermined amount oftime elapses, the thermostat 102 may begin processing the entries 130 inthe voice data log 118 for the recorded sound samples. In this case, thethermostat 102 proceeds to step 212 in response to determining not tocollect additional sound samples.

At step 212, the thermostat 102 iteratively selects an entry 130 fromthe voice data log 118 to process. Here, the thermostat 102 beginsselecting entries 130 from the voice data log 118 for processing. In oneembodiment, the selects entries 130 from the voice data log 118 in aFirst-In-First-Out (FIFO) order.

At step 214, the thermostat 102 determines whether a voice was detectedwithin the audio signature 134 that is associated with the selectedentry 130. In one embodiment, the thermostat 102 may analyze the audiosignature 134 to determine whether a voice was present within the audiosignal 306. The thermostat 102 proceeds to step 216 in response todetermining that a voice was not detected in the selected entry 130. Inthis case, the thermostat 102 determines that the recorded audio signal306 that corresponds with the selected entry 130 only includes noisesand does not include a voice that would indicate that a person waspresent within the space 300. For example, the audio signal 306 maycorrespond with an alarm going off, noises from pets, or music playing.At step 216, the thermostat 102 removes the selected entry 130 from thevoice data log 118. In this case, the thermostat 102 removes the entry130 since the corresponding audio signal 306 cannot be used to determinewhether a person was present within the space 300.

Returning to step 214, the thermostat 102 proceeds to step 218 inresponse to determining that a voice was detected in the selected entry130. In this case, the thermostat 102 determines that the recorded audiosignal 306 that corresponds with the selected entry 130 contains a voicewhich indicates that a person may be present within the space 300. Sincethe selected entry 130 contain a voice, the thermostat 102 will preservethe selected entry 130 for further processing.

At step 218, the thermostat 102 determines whether to select anotherentry 130 from the voice data log 118. In one embodiment, the thermostat102 may be configured to process a predetermined number of entries 130before proceeding with further processing. This process ensures that thethermostat 102 has a suitable number of entries 130 remaining to processafter filtering out the entries 130 that do not contain a voice. Thethermostat 102 returns to step 212 in response to determining to selectanother entry 130 from the voice data log 118. In this case, thethermostat 102 returns to step 212 to process additional entries 130.The thermostat 102 proceeds to step 220 in response to determining notto select another entry 130 from the voice data log 118. In this case,the thermostat 102 determines that a suitable amount of entries 130 areavailable for further processing and clustering.

At step 220, the thermostat 102 clusters the remaining entries 130 inthe voice data log 118. Here, the thermostat 102 begins clustering theremaining entries 130 in the voice data log 118 based on their audiosignatures 134. The thermostat 102 uses the audio signatures 134 todetermine entries 130 in the voice data log 118 have a common soundsource. In other words, the thermostat 102 uses the audio signatures 134to identify entries 130 that correspond with the same person speaking.This process allows the thermostat 102 to determine the number of peoplethat are present based on their voices. Each cluster corresponds with adifferent person that is present within the space. In one embodiment,the thermostat 102 may cluster the entries 130 in the voice data log 118using a Bayesian Information Criterion (BIC) greedy clusteringalgorithm. For example, the thermostat 102 may use audio signatures 134that are derived from MFCC features. In this example, the thermostat 102may compare the MFCC features from an entry 130 to the MFCC features ofthe other entries 130 in the voice data log 118 to identify entries withsimilar MFCC features. The thermostat 102 may then cluster the entries130 that have similar MFCC features. In other examples, the thermostat102 may cluster together entries 130 in the voice data log 118 using anyother suitable type or combination of criteria. In other embodiments,the thermostat 102 may cluster the entries 130 in the voice data log 118using any other suitable algorithm or technique.

Continuing with the example in FIG. 3, the thermostat 102 may identifyfour clusters. In this example, a first cluster corresponds with thefirst audio signal 306A, a second cluster corresponds with the secondaudio signal 306B, a third cluster corresponds with the third audiosignal 306C, and a fourth cluster corresponds with the fourth audiosignal 306D.

Returning to FIG. 2 at step 222, the thermostat 102 filters the clustersbased on the direction of arrival information. When multiple clustersshare the same direction of arrival 136 location, this indicates thatthese clusters share the same sound source. Typically, when the samesound source generates multiple voices this indicates that the soundsource in an electronic device (e.g. a television or radio) and that thesound source is not a person. Here, the thermostat 102 identifies theclusters that share a common sound source and removes the clusters fromconsideration. This process allows the thermostat 102 to filter out theelectronic devices which are not actual people that are present withinthe space 300.

Continuing with the example in FIG. 3, the thermostat 102 determinesthat the third cluster and the fourth cluster have the same direction ofarrival 136C. In this example, the thermostat 102 determines that thethird cluster and the fourth cluster correspond with the same soundsource (i.e. the television 304). This means that the audio signals 306(i.e. the third audio signal 306C and the fourth audio signal 306D)corresponding with the third cluster and the fourth cluster are from anelectronic device and are not from a person that is present in the space300. In this case, the thermostat 102 will remove the third cluster andthe fourth cluster from consideration. In some embodiments, thethermostat 102 may also remove the entries 130 that are associated withthe third cluster and the fourth cluster from the voice data log 118.

Returning to FIG. 2 at step 224, the thermostat 102 determines apredicted occupancy level based on the clusters. The thermostat 102 setsthe predicted occupancy level to the number of remaining clusters afterfiltering. Each cluster corresponds with a different person that ispresent within the space. Continuing with the example in FIG. 3, thethermostat 102 determines that there are two people present within thespace 300.

At step 226, the thermostat 102 controls the HVAC system 104 based onthe predicted occupancy level. As an example, the thermostat 102 may beconfigured to adjust a set point temperature based on the predictednumber of people within the space 300. In this example, the thermostat102 may increase or decrease a set point temperature for the space 300based on the predicted number of people that are present within thespace.

As another example, the thermostat 102 may be configured to transitionthe HVAC system 104 into a low power mode based on the predicted numberof people within the space 300. For instance, the thermostat 102 maytransition the HVAC system from a normal operation mode (e.g. a homemode) to a low power mode (e.g. an away mode) when the thermostat 102determines that no one is present in the space 300. This configurationallows the HVAC system 104 to conserve energy when no one is presentwithin the space 300.

As another example, the thermostat 102 may be configured to transitionthe HVAC system 104 out of a low power mode based on the predictednumber of people within the space 300. For instance, the thermostat 102may transition the HVAC system 104 from a low power mode (e.g. an awaymode) to a normal operation mode (e.g. a home mode) when the thermostat102 determines that someone is present within the space 300. Thisconfiguration allows HVAC system 104 to resume normal operation whensomeone is present within the space 300.

In other examples, the occupancy detection engine 110 may be configuredto perform any other suitable type of operation to control the operationof the HVAC system 104 based on the predicted number of people withinthe space 300.

Occupancy Tracking Process Using User Device Detection

FIG. 4 is a flowchart of an embodiment of an occupancy tracking process400 using user device detection. The occupancy tracking system 100 mayemploy process 400 to determine the number of people that are presentwithin a space based on the user devices that are detected within thespace. Unlike existing HVAC systems that rely on proximity sensors andmotion sensors, the occupancy tracking system 100 is able to determinewhich devices are connected to the access point 106 within a space andto determine the number of people that are present within the spacebased on the user devices that are detected. Process 400 also allows theoccupancy tracking system 100 to distinguish electronic devices (e.g.televisions and desktop computers) that are always present in a spacefrom personal user devices to provide a more accurate prediction of thenumber of people that are present within the space. Process 400 alsoallows the occupancy tracking system 100 to efficiently control theoperation of the HVAC system 104 based on the determined number ofpeople that are present within the space.

At step 402, the thermostat 102 establishes a network connection with anaccess point 106. As an example, the thermostat 102 may join a WiFinetwork that is associated with the access point 106. In other examples,the thermostat 102 may join a Zigbee network, a Z-wave network, an RFnetwork, or any other suitable type of network that is associated withthe access point 106. After establishing the network connection with theaccess point 106, the thermostat 102 is able to exchange (i.e. send andreceive) data with the access point 106. Referring to FIG. 5 as anexample, the thermostat 102 establishes a network connection with anaccess point 106 that is located within a space 500 (e.g. a home).

At step 404, the thermostat 102 obtains device information 504 fordevices that are connected to the network 108. Continuing with theexample in FIG. 5, the thermostat 102 may send a request 502 to theaccess point 106 to query the access point 106 about the devices thatare currently connected to the network 108. In response to receiving therequest 502, the access point 106 may scan (e.g. an Address ResolutionProtocol (ARP) scan) the network 108 to identify the devices that arecurrently connected to the network 108. After identifying the devicesthat are currently connected to the network 108, the access point 106sends the device information 504 about the identified devices to thethermostat 102. In some embodiments, the thermostat 102 may periodicallyreceive device information 504 from the access point 106 about thedevices that are currently connected to the network 108. In this case,the access point 106 may periodically scan the network 108 to identifythe devices that are currently connected to the network 108 and may senddevice information 504 about the identified devices to the thermostat102. The thermostat 102 may receive Media Access Control (MAC)addresses, Internet Protocol (IP) addresses, serial numbers, productnames, or any other suitable type of identifier for the devices that areconnected to the network 108. In this example, the thermostat 102receives device information 504 that identifies a first device 506 (e.g.a television) and a second device 508 (e.g. a desktop computer).

Returning to FIG. 4 at step 406, the thermostat generates entries 122 ina device log 116 for the devices that are connected to the network 108.Continuing with the example in FIG. 5, the thermostat 102 may create afirst entry 122 in the device log 116 for the first device 506 (e.g. thetelevision) and a second entry 122 in the device log 116 for the seconddevice 508 (e.g. the desktop computer). The first entry 122 may comprisea timestamp 124 that identifies the time when the first device 506 wasdetected and a device identifier 126 (e.g. a MAC address) for the firstdevice 506. Similarly, the second entry 122 may comprise a timestamp 124that identifies the time that the second device 508 was detected and adevice identifier 126 (e.g. a MAC address) for the second device 508.

Returning to FIG. 4 at step 408, the thermostat 102 determines whetherto continue scanning the network 108 for devices. In one embodiment, thethermostat 102 may be configured to continue scanning the network 108for a predetermined amount of time. The thermostat 102 may use a timerto determine whether the predetermined amount of time has elapsed. Thethermostat 102 determines to continue scanning the network 108 when thetimer has not expired. Otherwise, the thermostat 102 determines todiscontinue scanning the network 108 when the timer has elapsed. Thethermostat returns to step 404 in response to determining to continuescanning the network 108 for devices.

Referring to FIG. 6 as an example, the thermostat 102 may return step404 in response to determining to continue scanning the network 108 fordevices. In this example, the thermostat 102 sends another request 502to the access point 106 to request device information 504 thatidentifies the devices that are currently connected to the network 108.The thermostat 102 receives device information 504 that identifies thefirst device 506 (e.g. the television), the second device 508 (e.g. thedesktop computer), a third device 510, and a fourth device 514. Thethird device 510 is a smartphone for a first person 512 and the fourthdevice 514 is a smartphone for a second person 516. The thermostat 102will then create new entries 122 in the device log 116 for the firstdevice 506, the second device 508, the third device 510, and the fourthdevice 514.

Returning to FIG. 4 at step 408, the thermostat 102 proceeds to step 410in response to determining not to scan the network 108 for devices. Atstep 410, the thermostat 102 selects a device from the device log 116.After recording a list of devices that are present within the space 500over the predetermined period of time, the thermostat 102 then begins toclassify the devices. The thermostat 102 iteratively selects devicesfrom the device log 116 to determine a classification for the device. Inone embodiment, the thermostat 102 classifies a device as either a homedevice or a user device. A home device is a device that is usuallypresent within the space 500 most of the time. Examples of home devicesinclude, but are not limited to, televisions, radios, desktop computers,smart appliances, Internet-of-things devices, or any other suitable typeof device. A user device is a device that is associated with aparticular person that is not always present within the space 500. Inother words, a user device is a portable device that a person can takewith them when they leave the space 500. Examples of user devicesinclude, but are not limited to, smartphones, tablets, smartwatches,smart glasses, laptop computers, or any other suitable type of portabledevice. In this example, the thermostat 102 classifies a device based onthe amount of time that the device is present within the space 500.Devices that are present within the space 500 for most of the time areclassified as a home device. Whereas, devices that are not alwayspresent within the space 500 are classified as user devices.

At step 412, the thermostat 102 determines a presence value for theselected device over the predetermined time interval. The presence valueis a numerical value that indicates how long a device is present withinthe space 500. The thermostat 102 uses the timestamps 124 from theentries 122 that are associated with the selected device to determinehow long the selected device has been present within the space 500. Inone embodiment, the presence value identifies a percentage of time thatthe selected device was present within the space 500 over thepredetermined time interval. As an example, the predetermined timeperiod may be seven days. When the selected device has been presentwithin the space 500 for the entire seven-day time period, thethermostat 102 may determine that the presence value for the selecteddevice is 1.0 or 100%. When the selected device has been present withinthe space 500 for half of the seven-day time period, the thermostat 102may determine that the presence value of the selected device is 0.5 or50%. In other examples, the thermostat 102 may assign any other suitablepresence value to the selected device to indicate how long the selecteddevice was present within the space 500.

At step 414, the thermostat 102 determines whether the presence value isgreater than a presence threshold value. The present threshold valueindicates a maximum amount of time that a device can be present withinthe space 500 to be considered a user device. For example, the presencethreshold value may be set to a value of 0.99 or 99%. In this example, adevice is classified as a user device when the device is present withinthe space 500 less than 99% of the time (i.e. the predetermined timeinterval). Conversely, the device is classified as a home device whenthe device is present within the space 500 more than 99% of the time. Inother examples, the presence threshold value may be set to any othersuitable value for classifying devices. The thermostat 102 compares thedetermined presence value for the selected device to the presencethreshold value to determine a classification for the selected device.

The thermostat 102 proceeds to step 416 in response to determining thatthe presence value is greater than a presence threshold value. In thiscase, the thermostat 102 determines that the presence value that isassociated with the selected device exceeds the maximum amount of timethat a device can be present within the space 500 to be considered auser device. At step 416, the thermostat 102 classifies the selecteddevice as a home device in the device log 116.

Returning to step 414, the thermostat 102 proceeds to step 418 inresponse to determining that the presence value is less than a presencethreshold value. In this case, the thermostat 102 determines that thepresence value that is associated with the selected device less than themaximum amount of time that a device can be present within the space 500to be considered a user device. At step 418, the thermostat 102classifies the selected device as a user device in the device log 116.

At step 420, the thermostat 102 determines whether to select anotherdevice from the device log 116. The thermostat 102 may iterativelyselect devices from the device log 116 until all of the devices havebeen classified. The thermostat 102 may determine to select anotherdevice when one or more devices in the device log 116 have not beenclassified. Otherwise, the thermostat 102 may determine to not selectanother device when all of the devices in the device log 116 have beenclassified. The thermostat 102 returns to step 410 in response todetermining to select another device from the device log 116 toclassify. In this case, the thermostat 102 returns to step 410 to repeatthe process of selecting a device and classifying the device based onits presence value.

The thermostat 102 proceeds to step 422 in response to determining notto select another device from the device log 116. At step 422, thethermostat 102 filters out the home devices from the device log 116.Since home devices are not typically associated with any particularperson, the thermostat 102 filters or removes the devices from thedevice log 116 that are classified as a home device. As an example, thethermostat 102 may filter or remove the entries 122 from the device log116 that are associated with a home device classification. By filteringout the home devices, the thermostat 102 is able to isolate theremaining devices which are classified as user devices. This processallows the thermostat 102 to isolate the devices that may be uniquelyassociated with a person.

At step 424, the thermostat 102 clusters the user devices from thedevice log 116. In one embodiment, the thermostat 102 may cluster theuser devices based on a confidence level that one or more devices arepresent at the same time within the space 500. The thermostat 102computes a confidence level for each pair of user devices in the devicelog 116. As an example, the confidence level that a user device B ispresent when user device A is present can be determined using thefollowing expression

$\frac{P\left( {A,B} \right)}{P(A)}$

where P(A, B) equals the probability of user device A and user device Bbeing present within a space and P(A) is the probability of user deviceA being present within the space. The thermostat 102 may repeat thisprocess for pairs of entries 122 in the device log 116 over apredetermined period of time. For example, the thermostat 102 maycompute confidence levels for pairs of user devices over a span of oneor more days. A higher confidence level between a pair of user devicesindicates that the pair of user devices are often present at the sametime within the space 500. In this case, the pair of user devices likelybelong to the same person. A low confidence level between a pair of userdevices indicates that the pair of user devices are not often present atthe same time within the space 500. In this case, the pair of userdevices likely do not belong to the same person. Through this process,the thermostat 102 begins to cluster user devices that have a highconfidence level among the user devices. For example, the thermostat 102may compare the confidence levels between user devices to apredetermined threshold value to identify user devices that should beclustered together. In this example, the thermostat 102 may cluster userdevices that have a confidence level that is greater than or equal tothe predetermined threshold value. The thermostat 102 may use anysuitable predetermined threshold value for clustering user devices.After clustering, each cluster of user devices corresponds with adifferent person that is present within the space 500.

At step 426, the thermostat 102 identifies a primary device for eachcluster. In one embodiment, the thermostat 102 identifies a smartphonefrom among the user devices within each cluster and classifies thesmartphone as the primary device for the cluster. In other examples, thethermostat 102 may alternatively classify any other suitable type ofuser device as a primary device for a cluster. In one embodiment, thethermostat 102 may identify a smartphone based on a device identifierthat is associated with a user device. In another embodiment, thethermostat 102 may identify a smartphone based on the confidence levelsthat are associated with a user device. For example, when a user devicehas confidence levels that equal to zero for some period of time thismay indicate that the user device was present within the space 500without any other user devices and therefore is a primary device (e.g. asmartphone) for a person.

By identifying a smartphone from among the user devices within acluster, the thermostat 102 is able to associate additional informationabout a person with the user devices within a cluster. For example, thethermostat 102 may use the smartphone to determine a user identifier(e.g. a name) for the person associated with the user devices within acluster. After determining the user identifier for the person that isassociated with the user devices in the cluster, the thermostat 102 maystore an association between the user identifier and the other userdevices within the cluster. The thermostat 102 may also associate otherinformation such as user preference settings, schedule information, andset point temperature preferences with the user devices in the cluster.For example, the thermostat 102 may determine set point temperaturesettings for a person that is associated with the user identifier over apredetermined time period. The thermostat 102 may then associate thedetermined temperature settings with the entries 122 in the device log116 for the user devices that are associated with the person. In otherexamples, the thermostat 102 may associate any other suitable type ofinformation with the user devices that are associated with a person.

At step 428, the thermostat 102 determines a predicted occupancy levelbased on the number of identified clusters. The thermostat 102 sets thepredicted occupancy level to the number of identified clusters. Eachcluster corresponds with a different person that is present within thespace 500. Continuing with the example in FIG. 6, the thermostat 102determines that two people are present within the space 500.

At step 430, the thermostat 102 controls the HVAC system 104 based onthe predicted occupancy level. The thermostat 102 may control the HVACsystem 104 using a process similar to the process described in step 226of FIG. 2. For example, the thermostat 102 may adjust a set pointtemperature for the space 500 based on the predicted number of peoplewithin the space 500. As another example, the thermostat 102 maytransition the HVAC system 104 into a low power mode or out of a lowpower mode based on the predicted number of people within the space 500.In other examples, the thermostat 102 may be configured to perform anyother suitable type of operation to control the operation of the HVACsystem 104 based on the predicted number of people within the space 500.

In some embodiments, the thermostat 102 may identify a person that ispresent within the space 500 based on the user devices that arecurrently detected within the space 500. The thermostat 102 may then usepersonalized information that is associated with the person to controlthe HVAC system 104. For example, the thermostat 102 may identify a userdevice that is currently present within the space 500. The thermostat102 may then identify a user identifier that is associated with the userdevice in the device log 116. The thermostat 102 then identifies userpreference settings (e.g. temperature settings) that are associated withthe user identifier in the device log 116. The thermostat 102 may thenuse the user preference settings to control the operation of the HVACsystem 104.

Occupancy Tracking Process Using Wireless Signal Distortion

FIG. 7 is a flowchart of an embodiment of an occupancy tracking process700 using wireless signal distortion. The occupancy tracking system 100may employ process 700 to determine the number of people that arepresent within a space based on a signal strength of a networkconnection between thermostat 102 and the access point 106. Unlikeexisting HVAC systems that rely on proximity sensors and motion sensors,the occupancy tracking system 100 is able to monitor the signal strengthof the network connection over time to determine the number of peoplethat are present within the space based on the measured signal strength.In a first phase, the occupancy tracking system 100 collects trainingdata 138 that is used to train a machine learning model 120 to predictthe number of people that are present within a space based on wirelesssignal distortion information. The training data 138 comprises signalstrength measurements 144 over a period of time and statistical metadata146 for the signal strength measurements 144. In a second phase, theoccupancy tracking system 100 collects current wireless signaldistortion information (e.g. current signal strength measurements 144)and provides the collected information to the trained machine learningmodel 120 to obtain a predicted occupancy level for the space. Process700 allows the occupancy tracking system 100 to efficiently control theoperation of the HVAC system 104 based on the determined number ofpeople that are present within the space.

Machine Learning Model Training Phase

At step 702, the thermostat 102 establishes a network connection with anaccess point 106. As an example, the thermostat 102 may join a WiFinetwork that is associated with the access point 106. In other examples,the thermostat 102 may join a Zigbee network, a Z-wave network, an RFnetwork, or any other suitable type of network that is associated withthe access point 106. After establishing the network connection with theaccess point 106, the thermostat 102 is able to determine a signalstrength of the network connection with the access point 106. Referringto FIG. 8 as an example, the thermostat 102 establishes a networkconnection 802 with an access point 106 that is located within a space800 (e.g. a home).

At step 704, the thermostat 102 measures the signal strength of thenetwork connection 802 between the thermostat 102 and the access point106 over a predetermined time period. For example, the thermostat 102may determine a Received Signal Strength Indicator (RSSI) value thatindicates the signal strength of the network connection 802 over thepredetermined time period. In other examples, the thermostat 102 may useany other suitable type of metric for determining the signal strength ofthe network connection 802.

At step 706, the thermostat 102 generates one or more training dataentries 140 within the training data 138 for the measured signalstrength. In one embodiment, each training data entry 140 comprises atimestamp 142 that indicates when the signal strength was recorded and asignal strength measurement 144. As an example, the predetermined timeperiod may be ten minutes. In this example, the thermostat 102 may beconfigured to generate a training data entry 140 for each minute of thepredetermined time period. This means that the thermostat 102 willcreate ten training data entries 140 that each comprise a timestamp 142that identifies a time within the predetermined time period and thecorresponding signal strength measurement 144. In other examples, thepredetermined time period may be any other suitable amount of time. Inaddition, the thermostat 102 may also be configured to generate trainingdata entries 140 for any other suitable time interval.

At step 708, the thermostat 102 determines whether to collect moretraining data 138. In one embodiment, the thermostat 102 may beconfigured to continue collecting training data 138 for a predeterminedamount of time. The thermostat 102 may use a timer to determine whetherthe predetermined amount of time has elapsed. The thermostat 102determines to continue collecting training data 138 when the timer hasnot expired. The thermostat 102 determines to discontinue collectingtraining data 138 when the timer has elapsed. The thermostat 102 returnsto step 704 in response to determining to collect additional trainingdata 138. In this case, the thermostat 102 will continue to collecttraining data 138 until the predetermined amount of time has elapsed.

As an example, the thermostat 102 may return to step 704 to collectadditional training data 138. In the example shown in FIG. 9, two people(i.e. person 804 and person 806) are present in the space 800 while thethermostat 102 collects additional signal strength measurements 144. Thesignal strength of the network connection 802 between the thermostat 102and the access point 106 will diminish as the number of people withinthe space 800 increases. The reason for this is because each person inthe room creates a distortion that negatively impacts the signalstrength of the network connection 802 between the thermostat 102 andthe access point 106. For example, when a person passes between thethermostat 102 and the access point 106, the line of sight path forsignal communications between thermostat 102 and the access point 106becomes obscured and degrades the signal strength of the networkconnection 802. By learning the relationship between the number ofpeople that are present within the space 800 and the correspondingmeasured signal strength, the machine learning model 120 is able toaccurately predicted the number of people that are present within thespace 800 based on a measured signal strength.

Returning to FIG. 7 at step 708, the thermostat 102 proceeds to step 710in response to determining not to collect additional training data 138.At step 710, the thermostat 102 generates statistical metadata 146 forthe training data entries 140. The statistical metadata 146 comprisesstatistics that are associated with the signal strength measurements 144over the predetermined time period. For example, the statisticalmetadata 146 may comprise a mean value, a standard deviation value, askew value, a variance value, a maximum signal strength value, a minimumsignal strength value, or any other suitable type of statisticalinformation associated with the wireless signal distortion information.

At step 712, the thermostat 102 associates occupancy information withthe training data entries 140. Here, the thermostat 102 determines thenumber of people that were present within the space 800 while capturingthe training information. The thermostat 102 associates the number ofpeople that are within the space 800 with each of entries 140 in thetraining data 138. In one embodiment, the thermostat 102 may determinethe number of people that are present within the space 800 using aprocess similar to process 200 and/or process 400 that are described inFIGS. 2 and 4, respectively. For example, the thermostat 102 may detectone or more devices that are connected to the access point 106 and thendetermine the number of people that are present within the space 800based on the detected devices. As another example, the thermostat 102may capture an audio signal within the space 800, identify voices withinthe audio signal, and determine the number of people that are presentwithin the space 800 based on the identified voices. In anotherembodiment, the thermostat 102 may query a user to identify the numberof people that are present within the space 800 for each of the entries140 in the training data 138. In other embodiments, the thermostat 102may use any other suitable technique for determining the number ofpeople that were present within the space 800 for each of the entries140 in the training data 138.

At step 714, the thermostat 102 trains a machine learning model 120using the training data entries 140. The thermostat 102 may use anysuitable technique for training the machine learning model 120 using thetraining data 138 as would be appreciated by one of ordinary skill inthe art. After training the machine learning model 120, the machinelearning model 120 is configured to output a predicted occupancy levelfor the space 800 based on a current signal strength measurement 144 andstatistical metadata 146 for the current signal strength measurement144. In some embodiments, the thermostat 102 may continue to collecttraining data 138 and to train the machine learning model 120 inparallel or in the background while proceeding to step 716.

Occupancy Tracking Phase

After training the machine learning model 120, the thermostat 102 maybegin collecting current wireless signal distortion information (e.g.current signal strength measurements 144) to provide to the machinelearning model 120 to determine the number of people that are presentwithin the space 800. At step 716, the thermostat 102 measures a currentsignal strength 144 between the thermostat 102 and the access point 106.The thermostat 102 may measure the current signal strength 144 betweenthe thermostat 102 and the access point 106 using a process similar tothe process described in step 704.

At step 718, the thermostat 102 generates statistical metadata 146 forthe current signal strength measurement 144. The thermostat 102 maygenerate statistical metadata 146 for the current signal strengthmeasurement 144 using a process similar to the process described in step710.

At step 720, the thermostat 102 inputs the current signal strengthmeasurement 144 and the statistical metadata 146 into the machinelearning model 120. The machine learning model 120 is configured todetermine and output a predicted occupancy level for the space 800 basedon the current signal strength measurement 144 and the correspondingstatistical metadata 146. The predicted occupancy level indicates anumber of people that the machine learning model 120 predicts arecurrently present within the space 800 based on the provided input. Atstep 722, the thermostat 102 obtains a predicted occupancy level fromthe machine learning model 120.

At step 724, the thermostat 102 controls the HVAC system 104 based onthe predicted occupancy level. The thermostat 102 may control the HVACsystem 104 using a process similar to the process described in step 226of FIG. 2. For example, the thermostat 102 may adjust a set pointtemperature for the space 500 based on the predicted number of peoplewithin the space 500. As another example, the thermostat 102 maytransition the HVAC system 104 into a low power mode or out of a lowpower mode based on the predicted number of people within the space 500.In other examples, the thermostat 102 may be configured to perform anyother suitable type of operation to control the operation of the HVACsystem 104 based on the predicted number of people within the space 500.

Occupancy Tracking Process Using Environmental Information

FIG. 10 is a flowchart of an embodiment of an occupancy tracking process1000 using environmental information. The occupancy tracking system 100may employ process 1000 to determine the number of people that arepresent within a space based on a combination of environmentalinformation. In one embodiment, the occupancy tracking system 100 maydetermine the number of people that are present within a space based onthe voices that are heard within the space, the user devices that aredetected within the space, and/or the signal strength of a networkconnection between the thermostat 102 and the access point 106. In otherembodiments, the occupancy tracking system 100 may determine the numberof people that are present within the space based on any other suitablecombination of environmental information about the space. Process 1000allows the occupancy tracking system 100 to distinguish electronicdevices (e.g. televisions and desktop computers) that are typicallypresent in a space from personal user devices to provide a more accurateprediction of the number of people that are present within the space.Process 1000 also allows the occupancy tracking system 100 to filter outvoices that come from electronic sound sources (e.g. televisions andradios) rather than people to provide a more accurate prediction of thenumber of people that are present within the space. Process 1000 alsoallows the occupancy tracking system 100 to efficiently control theoperation of the HVAC system 104 based on the determined number ofpeople that are present within the space.

At step 1002, the thermostat 102 determines a first occupancy levelusing a voice-based occupancy detection process. In one embodiment, thethermostat 102 may determine the first occupancy level using a processsimilar to process 200 that is described in FIG. 2. As an example, thethermostat 102 may record sound samples over a period of time. The soundsamples may comprise one or more audio signals 306 that include thevoice of a person. The thermostat 102 computes an audio signature 134for each audio signals 306 and determines a direction of arrival 136 foreach audio signal 306. The thermostat 102 then populates entries 130 inthe voice data log 118 for the audio signal 306. The thermostat 102 thenidentifies one or more clusters for the entries 130 based on the voicesidentified in the audio signal 306 and a direction of arrival 136 thatis associated with each voice. The thermostat 102 then determines thenumber of clusters that were identified where each cluster correspondswith a person that is present within the space.

At step 1004, the thermostat 102 determines a second occupancy levelusing a device-based occupancy detection process. In one embodiment, thethermostat 102 may determine the second occupancy level using a processsimilar to process 400 that is described in FIG. 4. As an example, thethermostat 102 may identify one or more devices that are currentlyconnected to the access point 106 over a predetermined time period. Thethermostat 102 then populates entries 122 in the device log 116 for theidentified devices. The thermostat 102 will then identify one or moreclusters for the entries 122 of the device log 116 where each clusteridentifies devices that are present within the space at the same time.The thermostat 102 may then determine the number of clusters that areidentified where each cluster corresponds with a person that is presentwithin the space.

At step 1006, the thermostat 102 determines a third occupancy levelusing a wireless signal distortion-based occupancy detection process. Inone embodiment, the thermostat 102 may determine the third occupancylevel using a process similar to process 700 that is described in FIG.7. As an example, the thermostat 102 may capture wireless signaldistortion information for the network connection between the thermostat102 and the access point 106. The wireless signal distortion informationcomprises signal strength measurements 144 of the network connectionbetween the thermostat 102 and the access point 106 over a predeterminedtime period. The thermostat 102 then generates statistical metadata 146for the wireless signal distortion information. The thermostat 102inputs the wireless signal distortion information and the statisticalmetadata 146 for the wireless signal distortion information into amachine learning model 120 to obtain the third occupancy level from themachine learning model 120.

At step 1008, the thermostat 102 determines a predicted occupancy levelbased on the first occupancy level, the second occupancy level, and thethird occupancy level. The predicted occupancy level indicates apredicted number of people that are present within the space based onthe first occupancy level, the second occupancy level, and the thirdoccupancy level. In one embodiment, the thermostat 102 may determine thepredicted occupancy level by taking an average of the first occupancylevel, the second occupancy level, and the third occupancy level.

In other embodiments, the thermostat 102 may determine the predictedoccupancy level based on a consensus or a voting scheme between thefirst occupancy level, the second occupancy level, and the thirdoccupancy level. As an example, the first occupancy level may indicatethat two people are present within the space, the second occupancy levelmay indicate that three people are present within the space, and thethird occupancy level may indicate that two people are present withinthe space. In this example, the thermostat 102 may determine that thepredicted occupancy level is two people based on the first occupancylevel, the second occupancy level, and the third occupancy level. Inother embodiments, the thermostat 102 may use any other suitabletechnique for determining the predicted occupancy level based on thefirst occupancy level, the second occupancy level, and the thirdoccupancy level.

At step 1010, the thermostat 102 determines whether the predictedoccupancy level is greater than zero. The thermostat 102 proceeds tostep 1016 in response to determining that the predicted occupancy levelis not greater than zero. In this case, the thermostat 102 determinesthat no one is present within the space and transitions the HVAC system104 to a low power mode (e.g. an away mode) to conserve energy.Otherwise, the thermostat 102 proceeds to step 1012 in response todetermining that the predicted occupancy level is greater than zero. Inthis case, the thermostat 102 determines that at least one person ispresent within the space and adjusts the HVAC settings for the HVACsystem 104 based on the people that are present.

At step 1012, the thermostat 102 identifies one or more people that arepresent within the space. In one embodiment, the thermostat 102 mayidentify a person that is present within the space based on the userdevices that are currently present within the space. For example, thethermostat 102 may use a process similar to the process described instep 426 in FIG. 4 to identify user devices that are associated with aparticular person.

At step 1014, the thermostat 102 identifies HVAC settings that areassociated with the people that are present within the space. After thethermostat 102 identifies the people that are present within the space,the thermostat 102 may then use HVAC settings that are associated withthe people to control the HVAC system 104. For example, the thermostat102 may identify a user device that is currently present within thespace 500. The thermostat 102 may then identify a user identifier thatis associated with the user device in the device log 116. The thermostat102 then identifies user preference settings (e.g. temperature settings)that are associated with the user identifier in the device log 116. Thethermostat 102 may then use the user preference settings to control theoperation of the HVAC system 104.

At step 1016, the thermostat 102 controls the HVAC system 104 based onthe identified HVAC settings. The thermostat 102 may control the HVACsystem 104 using a process similar to the process described in step 226of FIG. 2. For example, the thermostat 102 may adjust a set pointtemperature for the space 500 based on the predicted number of peoplewithin the space and/or the HVAC settings for the people that presentwithin the space. As another example, the thermostat 102 may transitionthe HVAC system 104 into a low power mode or out of a low power modebased on the predicted number of people within the space. In otherexamples, the thermostat 102 may be configured to perform any othersuitable type of operation to control the operation of the HVAC system104 based on the predicted number of people within the space and/or theHVAC settings for the people that present within the space.

Hardware Configuration for an Occupancy Tracking Device

FIG. 11 is an embodiment of a device (e.g. thermostat 102) of anoccupancy tracking system 100. As an example, the thermostat 102comprises a processor 1102, a memory 114, a network interface 1104, amicrophone array 112, and a display 1108. The thermostat 102 may beconfigured as shown or in any other suitable configuration.

Processor

The processor 1102 comprises one or more processors operably coupled tothe memory 114. The processor 1102 is any electronic circuitryincluding, but not limited to, state machines, one or more centralprocessing unit (CPU) chips, logic units, cores (e.g. a multi-coreprocessor), field-programmable gate array (FPGAs), application-specificintegrated circuits (ASICs), or digital signal processors (DSPs). Theprocessor 1102 may be a programmable logic device, a microcontroller, amicroprocessor, or any suitable combination of the preceding. Theprocessor 1102 is communicatively coupled to and in signal communicationwith the memory 114, the network interface 1104, the microphone array112, and the display 1108. The one or more processors are configured toprocess data and may be implemented in hardware or software. Forexample, the processor 1102 may be 8-bit, 16-bit, 32-bit, 64-bit, or ofany other suitable architecture. The processor 1102 may include anarithmetic logic unit (ALU) for performing arithmetic and logicoperations, processor registers that supply operands to the ALU andstore the results of ALU operations, and a control unit that fetchesinstructions from memory and executes them by directing the coordinatedoperations of the ALU, registers and other components.

The one or more processors are configured to implement variousinstructions. For example, the one or more processors are configured toexecute occupancy detection instructions 1106 to implement the occupancydetection engine 110. In this way, processor 1102 may be aspecial-purpose computer designed to implement the functions disclosedherein. In an embodiment, the occupancy detection engine 110 isimplemented using logic units, FPGAs, ASICs, DSPs, or any other suitablehardware. The occupancy detection engine 110 is configured to operate asdescribed in FIGS. 1-10. For example, the occupancy detection engine 110may be configured to perform the steps of process 200, 400, 700, and1000 as described in FIGS. 2, 4, 7, and 10, respectively.

Memory

The memory 114 is operable to store any of the information describedabove with respect to FIGS. 1-10 along with any other data,instructions, logic, rules, or code operable to implement thefunction(s) described herein when executed by the processor 1102. Thememory 114 comprises one or more disks, tape drives, or solid-statedrives, and may be used as an over-flow data storage device, to storeprograms when such programs are selected for execution, and to storeinstructions and data that are read during program execution. The memory114 may be volatile or non-volatile and may comprise a read-only memory(ROM), random-access memory (RAM), ternary content-addressable memory(TCAM), dynamic random-access memory (DRAM), and static random-accessmemory (SRAM).

The memory 114 is operable to store occupancy detection instructions1106, device logs 116, voice data logs 118, machine learning models 120,training data 138, and/or any other data or instructions. The occupancydetection instructions 1106 may comprise any suitable set ofinstructions, logic, rules, or code operable to execute the occupancydetection engine 110. The device logs 116, the voice data logs 118, thetraining data 138, and the machine learning models 120 are configuredsimilar to the device logs 116, the voice data logs 118, the trainingdata 138, and the machine learning models 120 described in FIGS. 1-10.

Network Interface

The network interface 1104 is configured to enable wired and/or wirelesscommunications. The network interface 1104 is configured to communicatedata between the thermostat 102 and other devices (e.g. the HVAC system104 and the access point 106), systems, or domains. For example, thenetwork interface 1104 may comprise an NFC interface, a Bluetoothinterface, a Zigbee interface, a Z-wave interface, an RFID interface, aWIFI interface, a LAN interface, a WAN interface, a PAN interface, amodem, a switch, or a router. The processor 1102 is configured to sendand receive data using the network interface 1104. The network interface1104 may be configured to use any suitable type of communicationprotocol as would be appreciated by one of ordinary skill in the art.

Microphone Array

The microphone array 112 comprises a plurality of microphones. Eachmicrophone is configured to capture audio signals (e.g. voices) ofusers. The microphone array 112 may be configured to capture audiosignals continuously, at predetermined intervals, or on-demand. Themicrophone array 112 is operably coupled to the occupancy detectionengine 110 and provides captured audio signals to the occupancydetection engine 110 to send to the processor 1102 for processing.

Display

The display 1108 is configured to present visual information to userusing graphical objects. Examples of the display 1108 include, but arenot limited to, a liquid crystal display (LCD), a liquid crystal onsilicon (LCOS) display, a light emitting diode (LED) display, an activematrix OLED (AMOLED), an organic LED (OLED) display, a projectordisplay, or any other suitable type of display as would be appreciatedby one of ordinary skill in the art.

HVAC System Configuration

FIG. 12 is a schematic diagram of an embodiment of an HVAC system 104configured to integrate with an occupancy tracking system 100. The HVACsystem 104 conditions air for delivery to an interior space of abuilding or home. In some embodiments, the HVAC system 104 is a rooftopunit (RTU) that is positioned on the roof of a building and theconditioned air is delivered to the interior of the building. In otherembodiments, portions of the system may be located within the buildingand a portion outside the building. The HVAC system 104 may also includeheating elements that are not shown here for convenience and clarity.The HVAC system 104 may be configured as shown in FIG. 12 or in anyother suitable configuration. For example, the HVAC system 104 mayinclude additional components or may omit one or more components shownin FIG. 12.

The HVAC system 104 comprises a working-fluid conduit subsystem 1202 formoving a working fluid, or refrigerant, through a cooling cycle. Theworking fluid may be any acceptable working fluid, or refrigerant,including, but not limited to, fluorocarbons (e.g. chlorofluorocarbons),ammonia, non-halogenated hydrocarbons (e.g. propane), hydrofluorocarbons(e.g. R-410A), or any other suitable type of refrigerant.

The HVAC system 104 comprises one or more condensing units 1203. In oneembodiment, the condensing unit 1203 comprises a compressor 1204, acondenser coil 1206, and a fan 1208. The compressor 1204 is coupled tothe working-fluid conduit subsystem 1202 that compresses the workingfluid. The condensing unit 1203 may be configured with a single-stage ormulti-stage compressor 1204. A single-stage compressor 1204 isconfigured to operate at a constant speed to increase the pressure ofthe working fluid to keep the working fluid moving along theworking-fluid conduit subsystem 1202. A multi-stage compressor 1204comprises multiple compressors configured to operate at a constant speedto increase the pressure of the working fluid to keep the working fluidmoving along the working-fluid conduit subsystem 1202. In thisconfiguration, one or more compressors can be turned on or off to adjustthe cooling capacity of the HVAC system 104. In some embodiments, acompressor 1204 may be configured to operate at multiple speeds or as avariable speed compressor. For example, the compressor 1204 may beconfigured to operate at multiple predetermined speeds.

In one embodiment, the condensing unit 1203 (e.g. the compressor 1204)is in signal communication with a controller or thermostat 102 using awired or wireless connection. The thermostat 102 is configured toprovide commands or signals to control the operation of the compressor1204. For example, the thermostat 102 is configured to send signals toturn on or off one or more compressors 1204 when the condensing unit1203 comprises a multi-stage compressor 1204. In this configuration, thethermostat 102 may operate the multi-stage compressors 1204 in a firstmode where all the compressors 1204 are on and a second mode where atleast one of the compressors 1204 is off. In some examples, thethermostat 102 may be configured to control the speed of the compressor1204.

The condenser 1206 is configured to assist with moving the working fluidthrough the working-fluid conduit subsystem 1202. The condenser 1206 islocated downstream of the compressor 1204 for rejecting heat. The fan1208 is configured to move air 1209 across the condenser 1206. Forexample, the fan 1208 may be configured to blow outside air through theheat exchanger to help cool the working fluid. The compressed, cooledworking fluid flows downstream from the condenser 1206 to an expansiondevice 1210, or metering device.

The expansion device 1210 is configured to remove pressure from theworking fluid. The expansion device 1210 is coupled to the working-fluidconduit subsystem 1202 downstream of the condenser 1206. The expansiondevice 1210 is closely associated with a cooling unit 1212 (e.g. anevaporator coil). The expansion device 1210 is coupled to theworking-fluid conduit subsystem 1202 downstream of the condenser 1206for removing pressure from the working fluid. In this way, the workingfluid is delivered to the cooling unit 1212 and receives heat fromairflow 1214 to produce a treated airflow 1216 that is delivered by aduct subsystem 1218 to the desired space, for example, a room in thebuilding.

A portion of the HVAC system 104 is configured to move air across thecooling unit 1212 and out of the duct sub-system 1218. Return air 1220,which may be air returning from the building, fresh air from outside, orsome combination, is pulled into a return duct 1222. A suction side of avariable-speed blower 1224 pulls the return air 1220. The variable-speedblower 1224 discharges airflow 1214 into a duct 1226 from where theairflow 1214 crosses the cooling unit 1212 or heating elements (notshown) to produce the treated airflow 1216.

Examples of a variable-speed blower 1224 include, but are not limitedto, belt-drive blowers controlled by inverters, direct-drive blowerswith electronically commutated motors (ECM), or any other suitable typesof blowers. In some configurations, the variable-speed blower 1224 isconfigured to operate at multiple predetermined fan speeds. In otherconfigurations, the fan speed of the variable-speed blower 1224 can varydynamically based on a corresponding temperature value instead ofrelying on using predetermined fan speeds. In other words, thevariable-speed blower 1224 may be configured to dynamically adjust itsfan speed over a range of fan speeds rather than using a set ofpredetermined fan speeds. This feature also allows the thermostat 102 togradually transition the speed of the variable-speed blower 1224 betweendifferent operating speeds. This contrasts with conventionalconfigurations where a variable-speed blower 1224 is abruptly switchedbetween different predetermined fan speeds. The variable-speed blower1224 is in signal communication with the thermostat 102 using anysuitable type of wired or wireless connection 1227. The thermostat 102is configured to provide commands or signals to the variable-speedblower 1224 to control the operation of the variable-speed blower 1224.For example, the thermostat 102 is configured to send signals to thevariable-speed blower 1224 to control the fan speed of thevariable-speed blower 1224. In some embodiments, the thermostat 102 maybe configured to send other commands or signals to the variable-speedblower 1224 to control any other functionality of the variable-speedblower 1224.

The HVAC system 104 comprises one or more sensors 1240 in signalcommunication with the thermostat 102. The sensors 1240 may comprise anysuitable type of sensor for measuring the air temperature. The sensors1240 may be positioned anywhere within a conditioned space (e.g. a roomor building) and/or the HVAC system 104. For example, the HVAC system104 may comprise a sensor 1240 positioned and configured to measure anoutdoor air temperature. As another example, the HVAC system 104 maycomprise a sensor 1240 positioned and configured to measure a supply ortreated air temperature and/or a return air temperature. In otherexamples, the HVAC system 104 may comprise sensors 1240 positioned andconfigured to measure any other suitable type of air temperature.

The HVAC system 104 comprises one or more thermostats, for example,located within a conditioned space (e.g. a room or building). Athermostat may be a single-stage thermostat, a multi-stage thermostat,or any suitable type of thermostat as would be appreciated by one ofordinary skill in the art. The thermostat is configured to allow a userto input a desired temperature or temperature set point for a designatedspace or zone such as the room. The thermostat 102 may use informationfrom the thermostat such as the temperature set point for controllingthe compressor 1204 and the variable-speed blower 1224. The thermostatis in signal communication with the thermostat 102 using any suitabletype of wired or wireless communications. In some embodiments, thethermostat may be integrated with the thermostat 102.

While several embodiments have been provided in the present disclosure,it should be understood that the disclosed systems and methods might beembodied in many other specific forms without departing from the spiritor scope of the present disclosure. The present examples are to beconsidered as illustrative and not restrictive, and the intention is notto be limited to the details given herein. For example, the variouselements or components may be combined or integrated with another systemor certain features may be omitted, or not implemented.

In addition, techniques, systems, subsystems, and methods described andillustrated in the various embodiments as discrete or separate may becombined or integrated with other systems, modules, techniques, ormethods without departing from the scope of the present disclosure.Other items shown or discussed as coupled or directly coupled orcommunicating with each other may be indirectly coupled or communicatingthrough some interface, device, or intermediate component whetherelectrically, mechanically, or otherwise. Other examples of changes,substitutions, and alterations are ascertainable by one skilled in theart and could be made without departing from the spirit and scopedisclosed herein.

To aid the Patent Office, and any readers of any patent issued on thisapplication in interpreting the claims appended hereto, applicants notethat they do not intend any of the appended claims to invoke 35 U.S.C. §112(f) as it exists on the date of filing hereof unless the words “meansfor” or “step for” are explicitly used in the particular claim.

1. An occupancy tracking device, comprising: a network interfaceoperably coupled to a Heating, Ventilation, and Air Conditioning (HVAC)system, wherein the HVAC system is configured to control a temperatureof a space; and a processor operably coupled to the network interface,configured to: establish a network connection with an access point;capture wireless signal distortion information for the networkconnection, wherein the wireless signal distortion informationidentifies a signal strength of the network connection with the accesspoint over a first predetermined time period; generate a firststatistical metadata for the wireless signal distortion information,wherein the first statistical metadata comprises one or more statisticalmetrics that are associated with the signal strength of the networkconnection with the access point over the first predetermined timeperiod; input the wireless signal distortion information and the firststatistical metadata for the wireless signal distortion information intoa machine learning model, wherein: the machine learning model isconfigured to determine a predicted occupancy level based on thewireless signal distortion information and the first statisticalmetadata for the wireless signal distortion information; and thepredicted occupancy level indicates a number of people that are presentwithin with the space; obtain the predicted occupancy level from themachine learning model; control the HVAC system based on the predictedoccupancy level; capture training information for the networkconnection, wherein the training information identifies a signalstrength of the network connection with the access point over a secondpredetermined time period; generate a second statistical metadata forthe training information; determine a number of people that are presentwithin the space when capturing the training information; and train themachine learning model using the training information, the secondstatistical metadata for the training information, and the number ofpeople that are present within the space when capturing the traininginformation.
 2. (canceled)
 3. The device of claim 1, wherein determiningthe number of people that are present within the space when capturingthe training information comprises: detecting one or more user devicesthat are connected to the access point; and identifying a number ofpeople that are associated with the one or more user devices.
 4. Thedevice of claim 1, wherein determining the number of people that arepresent within the space when capturing the training informationcomprises: capturing an audio signal within the space; identifying oneor more voices within the audio signal; and determining a number ofpeople that are present within the space based on the one or morevoices.
 5. The device of claim 1, wherein controlling the HVAC systemcomprises: determining a number of people that are present within thespace is greater than zero; and transitioning the HVAC system out of alow power mode.
 6. The device of claim 1, wherein controlling the HVACsystem comprises: determining a number of people that are present withinthe space is zero; and transitioning the HVAC system to a low powermode.
 7. The device of claim 1, wherein controlling the HVAC systemcomprises adjusting a set point temperature for the space.
 8. Anoccupancy tracking method, comprising: establishing a network connectionwith an access point; capturing wireless signal distortion informationfor the network connection, wherein the wireless signal distortioninformation identifies a signal strength of the network connection withthe access point over a first predetermined time period; generating afirst statistical metadata for the wireless signal distortioninformation, wherein the first statistical metadata comprises one ormore statistical metrics that are associated with the signal strength ofthe network connection with the access point over the firstpredetermined time period; inputting the wireless signal distortioninformation and the first statistical metadata for the wireless signaldistortion information into a machine learning model, wherein: themachine learning model is configured to determine a predicted occupancylevel based on the wireless signal distortion information and the firststatistical metadata for the wireless signal distortion information; andthe predicted occupancy level indicates a number of people that arepresent within with a space; obtaining the predicted occupancy levelfrom the machine learning model; controlling a Heating, Ventilation, andAir Conditioning (HVAC) system based on the predicted occupancy level,wherein the HVAC system is configured to control a temperature of thespace; capturing training information for the network connection,wherein the training information identifies a signal strength of thenetwork connection with the access point over a second predeterminedtime period; generating a second statistical metadata for the traininginformation; determining a number of people that are present within thespace when capturing the training information; and training the machinelearning model using the training information, the second statisticalmetadata for the training information, and the number of people that arepresent within the space when capturing the training information. 9.(canceled)
 10. The method of claim 8, wherein determining the number ofpeople that are present within the space when capturing the traininginformation comprises: detecting one or more user devices that areconnected to the access point; and identifying a number of people thatare associated with the one or more user devices.
 11. The method ofclaim 8, wherein determining the number of people that are presentwithin the space when capturing the training information comprises:capturing an audio signal within the space; identifying one or morevoices within the audio signal; and determining a number of people thatare present within the space based on the one or more voices.
 12. Themethod of claim 8, wherein controlling the HVAC system comprises:determining a number of people that are present within the space isgreater than zero; and transitioning the HVAC system out of a low powermode.
 13. The method of claim 8, wherein controlling the HVAC systemcomprises: determining a number of people that are present within thespace is zero; and transitioning the HVAC system to a low power mode.14. The method of claim 8, wherein controlling the HVAC system comprisesadjusting a set point temperature for the space.
 15. An occupancytracking system, comprising: a Heating, Ventilation, and AirConditioning (HVAC) system, wherein the HVAC system is configured tocontrol a temperature of a space; an access point configured to transmitdata to a thermostat; and the thermostat in signal communication withthe HVAC system and the access point, configured to: establish a networkconnection with the access point; capture wireless signal distortioninformation for the network connection, wherein the wireless signaldistortion information identifies a signal strength of the networkconnection with the access point over a first predetermined time period;generate a first statistical metadata for the wireless signal distortioninformation, wherein the first statistical metadata comprises one ormore statistical metrics that are associated with the signal strength ofthe network connection with the access point over the firstpredetermined time period; input the wireless signal distortioninformation and the first statistical metadata for the wireless signaldistortion information into a machine learning model, wherein: themachine learning model is configured to determine a predicted occupancylevel based on the wireless signal distortion information and the firststatistical metadata for the wireless signal distortion information; andthe predicted occupancy level indicates a number of people that arepresent within with the space; obtain the predicted occupancy level fromthe machine learning model; control the HVAC system based on thepredicted occupancy level; capture training information for the networkconnection, wherein the training information identifies a signalstrength of the network connection with the access point over a secondpredetermined time period; generate a second statistical metadata forthe training information; determine a number of people that are presentwithin the space when capturing the training information; and train themachine learning model using the training information, the secondstatistical metadata for the training information, and the number ofpeople that are present within the space when capturing the traininginformation.
 16. (canceled)
 17. The system of claim 15, whereindetermining the number of people that are present within the space whencapturing the training information comprises: detecting one or more userdevices that are connected to the access point; and identifying a numberof people that are associated with the one or more user devices.
 18. Thesystem of claim 15, wherein determining the number of people that arepresent within the space when capturing the training informationcomprises: capturing an audio signal within the space; identifying oneor more voices within the audio signal; and determining a number ofpeople that are present within the space based on the one or morevoices.
 19. The system of claim 15, wherein controlling the HVAC systemcomprises: determining a number of people that are present within thespace is greater than zero; and transitioning the HVAC system out of alow power mode.
 20. The system of claim 15, wherein controlling the HVACsystem comprises adjusting a set point temperature for the space.